Add comprehensive model card

This PR adds a comprehensive model card for the DoctorAgent-RL model.

It includes:
- Relevant metadata: `pipeline_tag: text-generation` and `library_name: transformers`.
- A detailed description of the model and its key features.
- Links to the research paper ([DoctorAgent-RL: A Multi-Agent Collaborative Reinforcement Learning System for Multi-Turn Clinical Dialogue](https://huggingface.co/papers/2505.19630)) and the GitHub repository ([https://github.com/JarvisUSTC/DoctorAgent-RL](https://github.com/JarvisUSTC/DoctorAgent-RL)).
- An example of how to use the model with the `transformers` library for multi-turn clinical dialogue.
- Citation information.

This will make the model more discoverable and easier to use for the community.

README.md

@@ -1,3 +1,81 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+  - dialogue
+  - medical
+  - reinforcement-learning
+  - multi-agent
+---
+
+# DoctorAgent-RL: A Multi-Agent Collaborative Reinforcement Learning System for Multi-Turn Clinical Dialogue
+
+This repository contains the `DoctorAgent-RL` model, which is a reinforcement learning (RL)-based multi-agent collaborative framework designed to revolutionize clinical dialogue. The model is presented in the paper [DoctorAgent-RL: A Multi-Agent Collaborative Reinforcement Learning System for Multi-Turn Clinical Dialogue](https://huggingface.co/papers/2505.19630).
+
+**Code**: [https://github.com/JarvisUSTC/DoctorAgent-RL](https://github.com/JarvisUSTC/DoctorAgent-RL)
+
+<div align="center">
+  <img width="1231" alt="DoctorAgent-RL Framework" src="https://github.com/user-attachments/assets/bd9f676e-01f9-406c-881d-c2b9f45e62f3" />
+</div>
+
+## Introduction
+
+DoctorAgent-RL addresses the critical limitations of static clinical dialogue systems by modeling medical consultations as dynamic decision-making processes under uncertainty. It enables:
+
+1.  **Adaptive Information Gathering**: Intelligent adjustment of dialogue paths based on patient responses.
+2.  **Clinical Reasoning Alignment**: Autonomous development of interaction strategies consistent with medical logic.
+3.  **Overcoming Static Paradigms**: Moving beyond superficial pattern imitation in existing dialogue datasets.
+
+Through continuous multi-turn interactions between doctor and patient agents, optimized via reinforcement learning, DoctorAgent-RL achieves significant improvements in diagnostic accuracy and interaction efficiency.
+
+## Key Features
+
+-   🧠 **Multi-Agent Collaboration**: Doctor and patient agents with distinct roles and objectives.
+-   📈 **Dynamic Strategy Optimization**: Reinforcement learning-based policy updates for adaptive behavior.
+-   🎯 **Comprehensive Reward Design**: Multi-dimensional consultation evaluation metrics guiding optimal strategies.
+-   📊 **Medical Knowledge Integration**: Clinical reasoning logic embedded in decision-making processes.
+-   📄 **MTMedDialog Dataset**: The first English multi-turn medical consultation dataset designed with simulation capabilities.
+
+## Usage
+
+You can use the `DoctorAgent-RL` model with the Hugging Face `transformers` library for text generation in a multi-turn dialogue context.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# Load the model and tokenizer
+model_id = "Jarvis1111/DoctorAgent-RL" 
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
+
+# Prepare a sample conversation
+messages = [
+    {"role": "user", "content": "Hello Doctor, I have a headache and feel tired."},
+]
+
+# Apply the chat template defined in the tokenizer_config.json
+# This is crucial for proper multi-turn dialogue with Qwen models
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+
+# Generate response
+input_ids = tokenizer(text, return_tensors="pt").input_ids.to(model.device)
+output = model.generate(input_ids, max_new_tokens=256, do_sample=True, temperature=0.7, top_p=0.9)
+response = tokenizer.decode(output[0], skip_special_tokens=True)
+
+print(response)
+```
+
+## Citation
+
+If DoctorAgent-RL contributes to your research, please consider citing our work:
+
+```latex
+@article{feng2025doctoragent,
+  title={DoctorAgent-RL: A Multi-Agent Collaborative Reinforcement Learning System for Multi-Turn Clinical Dialogue},
+  author={Feng, Yichun and Wang, Jiawei and Zhou, Lu and Li, Yixue},
+  journal={arXiv preprint arXiv:2505.19630},
+  year={2025}
+}
+```